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Course: Massive Data Analysis 2014

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(Week 5 -- Oct 6: Cloud computing, Map Reduce and Hadoop)
(Week 5 -- Oct 6: Cloud computing, Map Reduce and Hadoop)
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== Week 5 -- Oct 6: Cloud computing, Map Reduce and  Hadoop ==
== Week 5 -- Oct 6: Cloud computing, Map Reduce and  Hadoop ==
* Lecture notes:   
* Lecture notes:   
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** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/mapreduce-intro.pdf
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** http://vgc.poly.edu/~fchirigati/mda-class/mapreduce-intro.pdf
* Lab: after the lecture, you will work on an in-class exercise. For this you need to install Hadoop on your laptop and have your account setup on AWS. See instructions below.
* Lab: after the lecture, you will work on an in-class exercise. For this you need to install Hadoop on your laptop and have your account setup on AWS. See instructions below.

Revision as of 12:47, 3 October 2014

Contents

CS-GY 6333 Massive Data Analysis: Tentative Schedule -- subject to change

  • Lecture: Mondays, 1:00pm-3:25pm at 2MTC, room 9.011.

News

  • On Sept 22nd, I distributed AWS tokens that will be needed for your assignments. If you have not received your token, let me know.
  • Your first assignment has been posted -- see details below and in NYU Classes.

Background (4 weeks)

Week 1 -- Sept 8: Course Overview; the evolution of Data Management

Week 2 -- Sept 15: Provenance and Reproducibility

  • Github setup:

Week 3 -- Sept 22: Introduction to Databases; Relational Model and SQL

Week 4 -- Sept 29: Overview: Advanced SQL and Query Optimization

Big Data Foundations and Infrastructure (3 weeks)

Week 5 -- Oct 6: Cloud computing, Map Reduce and Hadoop

  • Lab: after the lecture, you will work on an in-class exercise. For this you need to install Hadoop on your laptop and have your account setup on AWS. See instructions below.


  • Required reading:
    • Data-Intensive Text Processing with MapReduce, Chapters 1 and 2
    • Mining of Massive Datasets (2nd Edition), Chapter 2 - 2.1 and 2.2 (Large-Scale File Systems and Map-Reduce).

Week 6 -- Oct 13: Fall Break

Week 7 -- Oct 20: Algorithm Design for MapReduce

  • Required reading:
    • Data-Intensive Text Processing with MapReduce, Chapters 1 and 2
    • Mining of Massive Datasets (2nd Edition), Chapter 2.


Week 8 -- Oct 27: Parallel Databases vs MapReduce, Query Processing on Mapreduce and High-level Languages



Big Data Algorithms and Techniques (3 weeks)

Week 9 -- Nov 3: Association Rules


Week 10 -- Nov 10: Finding similar items


Week 11 -- Nov 17: Graph Analysis


Week 12 -- Nov 25: Large-Scale Visualization -- Invited lecture by Dr. Lauro Lins (AT&T Research)

  • Reading:

The Value of Visualization, Jarke Van Wijk http://www.win.tue.nl/~vanwijk/vov.pdf

Tamara Munzner's Book draft 2 available online http://www.cs.ubc.ca/~tmm/courses/533/book/

Nanocubes Paper http://nanocubes.net http://nanocubes.net/assets/pdf/nanocubes_paper_preprint.pdf


Week 13 -- Dec 1: Data Cleaning and Integration

Week 14 -- Dec 8: Project Presentations

Week 15 -- Dec 15: Project Presentations

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